MAR stands for “Missing at Random,” which is another type of missing data mechanism in which the missingness of data is related to other variables in the dataset but not the missing values themselves. In other words, the probability of missing data depends on the observed variables in the data set but not on the missing data points themselves.
For example, suppose we have a dataset that includes information about income and education level. Suppose that people with higher education levels are more likely to not report their income, and people with lower income levels are more likely to report their income accurately. In this scenario, the missingness of the income data would be considered MAR because it is related to the education level of the respondent but not to their actual income.
When data are MAR, missing data can introduce bias into statistical analyses if not handled properly. However, methods such as maximum likelihood estimation or multiple imputation can be used to account for the missing data and minimize bias.
It is important to note that MAR does not imply that the missing data are ignorable or that ignoring them would not lead to biased estimates. Instead, it means that the missingness can be explained by other variables in the dataset, and that these variables can be included in the analysis to account for the missing data.